Modified multiple generalized regression neural network models using fuzzy C-means with principal component analysis for noise prediction of offshore platform

Chin, Cheng Siong, Ji, Xi, Woo, Wai Lok, Kwee, Tiaw Joo and Yang, Wenxian (2019) Modified multiple generalized regression neural network models using fuzzy C-means with principal component analysis for noise prediction of offshore platform. Neural Computing and Applications, 31 (4). pp. 1127-1142. ISSN 0941-0643

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Abstract

A modified multiple generalized regression neural network (GRNN) is proposed to predict the noise level of various compartments onboard of the offshore platform. With limited samples available during the initial design stage, GRNN can cause errors when it maps the available inputs to sound pressure level for the entire offshore platform. To obtain more relevant group for GRNNs training, fuzzy C-mean (FCM) is used. However, outliers in some group may interfere the prediction accuracy. The problem of selecting suitable inputs parameters (in each cluster) is often impeded by lack of accurate information. Principal component analysis (PCA) is used to ensure high relevance input variables in each cluster. By fusing multiple GRNNs by an optimal spread parameter, the proposed modeling scheme becomes quite effective for modeling multiple frequency-dependent data set (ranging from 125 to 8000 Hz) with different input parameters. The performance of FCM-PCA-GRNNs has improved significantly as the results show a 25% improvement on the spatial sound pressure level (SPL) and 85% improvement on the spatial average SPL than just GRNNs alone. By comparing with data obtained from real engine room on a jack-up rig, the FCM-PCA-GRNNs noise model performs better with around 16% less error than the empirical-based acoustic models. Additionally, the results show comparable performance to statistical energy analysis that requires more time and resources to solve during the early stage of the offshore platform design.

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